Over the last two decades, the research community has widely explored the field of anomaly detection in time-series. The latter can be addressed from five different perspectives. As reported in Section , we distinguish between clustering-based, density-estimation-based, distance-based, reconstruction-based, and forecasting-based techniques. Earlier techniques have investigated these five different paradigms by exploiting traditional machine learning [1]}, [2]} and statistical tools [3]}. Nevertheless, as mentioned in [4]}, these approaches have shown a drop in performance when dealing with high-dimensional time series. Given the recent advances in DL, DNNs have been considered as an alternative [5]}, [6]}, [7]}, [8]}, mainly taking inspiration from traditional methods. In the following, we review these five categories of approaches, starting with conventional techniques, then moving to current DL methods.
